FragDPI: a novel drug-protein interaction prediction model based on fragment understanding and unified coding

Abstract

Prediction of drug-protein binding is critical for virtual drug screening. Many deep learning methods have been proposed to predict the drug-protein binding based on protein sequences and drug representation sequences. However, most existing methods extract features from protein and drug sequences separately. As a result, they can not learn the features characterizing the drug-protein interactions. In addition, the existing methods encode the protein (drug) sequence usually based on the assumption that each amino acid (atom) has the same contribution to the binding, ignoring different impacts of different amino acids (atoms) on the binding. However, the event of drug-protein binding usually occurs between conserved residue fragments in the protein sequence and atom fragments of the drug molecule. Therefore, a more comprehensive encoding strategy is required to extract information from the conserved fragments.

In this paper, we propose a novel model, named FragDPI, to predict the drug-protein binding affinity. Unlike other methods, we encode the sequences based on the conserved fragments and encode the protein and drug into a unified vector. Moreover, we adopt a novel two-step training strategy to train FragDPI. The pre-training step is to learn the interactions between different fragments using unsupervised learning. The fine-tuning step is for predicting the binding affinities using supervised learning. The experiment results have illustrated the superiority of FragDPI.

Graphical abstract

Keywords

affinity score / drug-protein interaction / BERT / Bi-Transformer / virtual drug screening

Zhihui Yang is a PhD candidate in the School of Computer Science, Wuhan University, China. His current research interests include synthetic biology, deep learning, metabolic pathway reconstruction, and metabolic flux analysis

Juan Liu is a professor in the School of Computer Science, Wuhan University, China. Her research interests include machine learning, data mining, bioinformatics, pattern recognition, and artificial intelligence methods for medicine

Xuekai Zhu is a master’s student in the School of Computer Science, Wuhan University, China. His current research interests are in artificial intelligence methods for bioinformatics

Feng Yang is a PhD candidate in the School of Computer Science, Wuhan University, China. His current research interests include machine learning, retrosynthesis prediction and metabolic pathway design

Qiang Zhang is a PhD candidate in the School of Computer Science, Wuhan University, China. Her current research interests include retrosynthesis prediction, metabolic pathway design, bioinformatics, and machine learning

Hayat Ali Shah received his MS degree in Computer Science from Virtual University of Pakistan, Pakistan in 2018. He is currently a PhD candidate in the School of Computer Science, Wuhan University, China. His research interests are simulated alignments, multiple sequence alignments, machine learning, prediction and reconstruction of metabolic pathways

References

[1] Swinney D C, Anthony J. How were new medicines discovered? Nature Reviews Drug Discovery, 2011, 10(7): 507–519

[2] Gupta S, Jadaun A, Kumar H, Raj U, Varadwaj P K, Rao A R . Exploration of new drug-like inhibitors for serine/threonine protein phosphatase 5 of Plasmodium falciparum: a docking and simulation study. Journal of Biomolecular Structure and Dynamics, 2015, 33( 11): 2421–2441

[3] Yuriev E, Agostino M, Ramsland P A . Challenges and advances in computational docking: 2009 in review. Journal of Molecular Recognition, 2011, 24( 2): 149–164

[4] Huang K, Fu T, Glass L M, Zitnik M, Xiao C, Sun J. DeepPurpose: a deep learning library for drug-target interaction prediction. Bioinformatics, 2020, 36(22−23): 5545−5547

[5] Huang K, Xiao C, Glass L M, Sun J . MolTrans: molecular interaction transformer for drug-target interaction prediction. Bioinformatics, 2021, 37( 6): 830–836

[6] Zhao Q, Xiao F, Yang M, Li Y, Wang J. AttentionDTA: prediction of drug−target binding affinity using attention model. In: Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2019, 64−69

[7] Liao Z, You R, Huang X, Yao X, Huang T, Zhu S. DeepDock: enhancing ligand-protein interaction prediction by a combination of ligand and structure information. In: Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2019, 311−317

[8] Bai F, Morcos F, Cheng R R, Jiang H, Onuchic J N . Elucidating the druggable interface of protein-protein interactions using fragment docking and coevolutionary analysis. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113( 50): E8051–E8058

[9] Yao H, Song Y, Chen Y, Wu N, Xu J, Sun C, Zhang J, Weng T, Zhang Z, Wu Z, Cheng L, Shi D, Lu X, Lei J, Crispin M, Shi Y, Li L, Li S . Molecular architecture of the SARS-CoV-2 virus. Cell, 2020, 183( 3): 730–738.e13

[10] Shu X, Royant A, Lin M Z, Aguilera T A, Lev-Ram V, Steinbach P A, Tsien R Y . Mammalian expression of infrared fluorescent proteins engineered from a bacterial phytochrome. Science, 2009, 324( 5928): 804–807

[11] Pahikkala T, Airola A, Pietila S, Shakyawar S, Szwajda A, Tang J, Aittokallio T . Toward more realistic drug-target interaction predictions. Briefings in Bioinformatics, 2015, 16( 2): 325–337

[12] Zheng X, Ding H, Mamitsuka H, Zhu S. Collaborative matrix factorization with multiple similarities for predicting drug-target interactions. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013, 1025−1033

[13] Özturk H, Özgür A, Ozkirimli E . DeepDTA: deep drug-target binding affinity prediction. Bioinformatics, 2018, 34( 17): i821–i829

[14] Nguyen T, Le H, Venkatesh S . GraphDTA: prediction of drug−target binding affinity using graph convolutional networks. BioRxiv, 2019, 684662

[15] Devlin J, Chang M W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019, 4171−4186

[16] Dong L, Yang N, Wang W, Wei F, Liu X, Wang Y, Gao J, Zhou M, Hon H W. Unified language model pre-training for natural language understanding and generation. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019, 1170

[17] Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language models are unsupervised multitask learners. OpenAI blog, 2019, 1(8): 9

[18] Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu P J . Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 2020, 21: 1–67

[19] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 6000−6010

[20] Karimi M, Wu D, Wang Z, Shen Y . DeepAffinity: interpretable deep learning of compound−protein affinity through unified recurrent and convolutional neural networks. Bioinformatics, 2019, 35( 18): 3329–3338

[21] Liu T, Lin Y, Wen X, Jorissen R N, Gilson M K . BindingDB: a web-accessible database of experimentally determined protein−ligand binding affinities. Nucleic Acids Research, 2007, 35( S1): D198–D201

[22] Kuhn M, Von Mering C, Campillos M, Jensen L J, Bork P . STITCH: interaction networks of chemicals and proteins. Nucleic Acids Research, 2008, 36( S1): D684–D688

[23] Suzek B E, Wang Y, Huang H, McGarvey P B, Wu C H, UniProt Consortium . UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics, 2015, 31( 6): 926–932

[24] Li M, Lu Z, Wu Y, Li Y . BACPI: a bi-directional attention neural network for compound−protein interaction and binding affinity prediction. Bioinformatics, 2022, 38( 7): 1995–2002

[25] Leonard T A, Różycki B, Saidi L F, Hummer G, Hurley J H . Crystal structure and allosteric activation of protein kinase C βII. Cell, 2011, 144( 1): 55–66

[26] Sutton R B, Sprang S R . Structure of the protein kinase cβ phospholipid-binding C2 domain complexed with Ca²⁺. Structure, 1998, 6( 11): 1395–1405

[27] Thao T T N, Labroussaa F, Ebert N, V’kovski P, Stalder H, Portmann J, Kelly J, Steiner S, Holwerda M, Kratzel A, Gultom M, Schmied K, Laloli L, Hüsser L, Wider M, Pfaender S, Hirt D, Cippà V, Crespo-Pomar S, Schröder S, Muth D, Niemeyer D, Corman V M, Müller M A, Drosten C, Dijkman R, Jores J, Thiel V . Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform. Nature, 2020, 582( 7813): 561–565

[28] Tzenaki N, Papakonstanti E A . p110δ PI3 kinase pathway: emerging roles in cancer. Frontiers in Oncology, 2013, 3: 40

[29] Takahashi Y, Hayakawa A, Sano R, Fukuda H, Harada M, Kubo R, Okawa T, Kominato Y . Histone deacetylase inhibitors suppress ACE2 and ABO simultaneously, suggesting a preventive potential against COVID-19. Scientific Reports, 2021, 11( 1): 3379

[30] Volz H P, Gleiter C H . Monoamine oxidase inhibitors. Drugs & Aging, 1998, 13( 5): 341–355

[31] Kumar A, Redondo-Muñoz J, Perez-García V, Cortes I, Chagoyen M, Carrera A C . Nuclear but not cytosolic phosphoinositide 3-kinase beta has an essential function in cell survival. Molecular and Cellular Biology, 2011, 31( 10): 2122–2133

Acknowledgements

This work was supported by the National Key R&D Program of China (2019YFA0904303).

RIGHTS & PERMISSIONS

2023 Higher Education Press